CSV Row Duplicator
Working with CSV files can feel like herding cats—especially when you need to duplicate specific rows for testing, analysis, or bulk data entry. The CSV Row Duplicator is here to save the day! Simply upload your CSV file, pick the row you want to copy, and specify how many duplicates you need. Whether you're prepping data for a report, running simulations, or just trying to make your spreadsheet life easier, this tool handles it all in a few clicks. No more manual copying or spreadsheet headaches—just smooth, efficient data manipulation. Let’s make your CSV workflows a breeze!
Upload a CSV file, select a row, and duplicate it as many times as needed.
Preview
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How It Works
The CSV Row Duplicator follows a simple process: you upload your CSV file, select the row you want to duplicate, and specify how many copies you need. The tool then inserts the duplicated rows right where you want them, giving you a clean, updated CSV file ready for download. Here's a quick breakdown of the steps:
- Upload your CSV file using the "Upload CSV File" button.
- Preview the first few rows to ensure the file is correct.
- Enter the row number you want to duplicate.
- Specify how many times you want to duplicate the row.
- Click "Duplicate Row" and preview the updated file.
- Download the modified CSV file when you're ready.
Example Table
| Original Row | Duplicated Rows (3x) |
|---|---|
| Row 1: Data A, Data B, Data C | Row 1: Data A, Data B, Data C Row 1: Data A, Data B, Data C Row 1: Data A, Data B, Data C |
| Row 2: Data D, Data E, Data F | Row 2: Data D, Data E, Data F Row 2: Data D, Data E, Data F Row 2: Data D, Data E, Data F |
10 Common Use Cases for the CSV Row Duplicator
- Creating test datasets for software development.
- Generating bulk data for simulations or modeling.
- Preparing sample data for presentations or reports.
- Duplicating rows for statistical analysis.
- Expanding datasets for machine learning training.
- Replicating rows for database testing.
- Creating placeholder data for templates.
- Filling in gaps in incomplete datasets.
- Preparing data for bulk email campaigns.
- Simplifying repetitive data entry tasks.